International Journal of Advanced Mechatronic Systems (7 papers in press)

Regular Issues

• A novel blockchain approach to identify child malnutrition using residual pyramid forward fractional network  
  by Prateeksha Chouksey, Prasadu Peddi, Sandeep Kadam 
  Abstract: Malnutrition occurs due to a lack of nutrients, which is often curable with early detection. However, detecting malnutrition at an early stage is challenging and ineffective in various techniques. Overfitting also affects the previous methods performance. Therefore, here the residual pyramid forward fractional network (RPFF-Net) is developed for child malnutrition. Initially, the child's health is recorded in the blocks of the blockchain. Then, the recorded data is saved in the cloud server, which holds the tracking architecture. Next, the data is normalised by Z-score normalisation. After that, the features are selected by employing an ensemble-based model with mutual information, information gain (IG), and recursive feature elimination (RFE). After that, nutrition status is tracked by the RPFF-Net approach, which is created by fusing PyramidNet, deep residual network (DRN), and fractional calculus (FC). The RPFF-Net attained a true positive rate (TPR), accuracy, and true negative rate (TNR) of 91.87%, 90.59%, and 90.98%.
  Keywords: malnutrition; Z-score normalisation; deep residual network; DNR; pyramid network; fractional calculus; FC.
  DOI: 10.1504/IJAMECHS.2026.10072878
   
  
• Hybridisation of nonlinear autoregressive model with deep long short-term memory network for crop damage detection using time series data  
  by Saravanan Radhakrishnan, V. Vijayarajan 
  Abstract: Climate change and population growth have intensified crop damage in recent years. This paper introduced nonlinear autoregressive model with exogenous inputs fused with deep long-short term memory (NARX-DLSTM) for detecting crop damage at an early stage. The input time series data undergoes preprocessing, followed by the extraction of technical indicators like relative strength index (RSI), double exponential moving average (DEMA), weighted moving average (WMA), simple moving average (SMA), Welles Wilders smoothing average (WWS), moving average convergence divergence (MACD), linear regression forecast (LRF) and lowest low value (LL). Then, feature selection is performed using weighted Euclidean distance (WED), and data augmentation is applied through synthetic minority over-sampling technique (SMOTE). Finally, NARX-DLSTM is performed which is the combination of DLSTM and NARX recurrent neural networks, achieves mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) of 0.200, 0.447, 0.165, and 0.114.
  Keywords: weighted Euclidean distance; WED; crop damage detection; deep long-short term memory; DLSTM; synthetic minority over-sampling technique; SMOTE; recurrent neural networks; RNNs.
  DOI: 10.1504/IJAMECHS.2026.10073117
   
  
• Enhancing the efficiency of 4D printing design through time-series prediction by dense-LSTM crossed network  
  by Yifan Xu, Mengtao Wang, Hidemitsu Furukawa, Zhongkui Wang, Qi Li, Lin Meng 
  Abstract: This paper proposes a deep learning-based approach to predict the deformation of 4D-printed hydrogel models with varying lengths, aiming to improve the efficiency of the design process. A voxel-based modeling method is used to create hydrogel models in Abaqus, and their deformation data is obtained through finite element analysis (FEA). A mixed dataset is then constructed by mapping each model's expansion rate sequence to its corresponding deformation outcome. Based on this dataset, a novel deep learning architecture called dense-LSTM crossed network (DSCN) is introduced and trained. The trained model enables direct prediction of deformation results from the initial model parameters, reducing reliance on time-consuming simulation. Experimental results show that the proposed method shortens the model design verification process by 1.5–2%, thus enhancing the overall efficiency of 4D printing design. This study demonstrates the potential of combining intelligent modeling with deep learning to streamline additive manufacturing workflows involving shape-morphing materials.
  Keywords: 4D printing; deep learning; hydrogel; recurrent neural network; RNN; voxelisation design; mixed dataset.
  DOI: 10.1504/IJAMECHS.2025.10071224
   
  
• Improving the energy efficiency of coal fired power plants using deep learning with optimisation algorithm  
  by Arvind Kumar Tiwari, Anupama, Arbind Kumar Amar, Chandan Kumar 
  Abstract: Reducing CO₂ emissions from coal-fired power plants (CFPP) is crucial for sustainable energy production. Continuous emission monitoring systems (CEMS) are commonly used but are costly and complex. This paper proposes the integration of lignite coal with biomass to reduce emissions and enhance efficiency. Additionally, optimising feed water heaters (FWH) minimises heat transients and fuel consumption. Key parameters such as reheat steam temperature, turbine extraction pressure and temperature, and combustion ratio are considered. To further optimise energy efficiency, an artificial neural network (ANN) enhanced with the improved seagull optimisation algorithm (ISOA) is employed. Compared to ANN, GA, and PSO, the proposed approach significantly reduces system errors and improves overall plant efficiency. Experimental results demonstrate an energy efficiency of 98.95%, highlighting the effectiveness of CFPP optimisation with deep learning and advanced optimisation techniques.
  Keywords: coal-fired power plants; CFPP; artificial neural network; ANN; deep learning; DL; seagull optimisation algorithm; SOA; energy efficiency; EF; ultra-supercritical power plant; coal with biomass; carbon dioxide emissions; feed water heater; thermal efficiency.
  DOI: 10.1504/IJAMECHS.2025.10072991
   
  
• Optimising production efficiency in small and medium enterprises with IoT-driven automated jerry can filling systems  
  by Anshu Prakash Murdan, Dhanveer Ramjus 
  Abstract: This paper presents a cost-effective, IoT-based automated filling and capping system tailored specifically for small and medium-sized enterprises (SMEs). The solution leverages open-source Arduino controllers, modular hardware, and advanced sensor integration, including ultrasonic and infrared sensors, to enable precise, real-time operational adjustments. While traditional automation technologies such as PLCs and SCADA remain widely used in industries, this system offers a more affordable alternative, providing superior scalability, flexibility, and ease of maintenance. Comprehensive operational and field testing, supported by statistical analyses, demonstrated substantial improvements including a 60% increase in throughput, an 85% improvement in filling accuracy, and halving material waste and downtime. Economic analysis further validated the financial advantages, highlighting a rapid return on investment. Operator feedback emphasised its reliability and intuitive interface. By integrating real-time monitoring, adaptive control algorithms, and predictive maintenance capabilities, the developed system establishes a new benchmark for accessible, efficient, and scalable automation solutions suitable for SMEs.
  Keywords: automation; internet of things; IoT; prototype; real-time monitoring; sensor calibration; cost-effectiveness.
  DOI: 10.1504/IJAMECHS.2025.10071770
   
  
• Classification of malware family in large executable files using NeASA-Net in MapReduce framework  
  by Manoj D. Shelar, S. Srinivasa Rao 
  Abstract: This paper proposes the neuron attention stacked autoencoder (NeASA-Net) to classify the malware family from the executable files. The classification process is done in the MapReduce framework. At first, the accumulated input executable files are subjected to the mapper phase. Here, the features, like opcode 4-gram, API 4-gram, file size, and PE section size are determined. Then, the determined features are merged and subjected to malware family classification using NeASA-Net in the reducer phase. The NeASA-Net is introduced by combining deep stacked autoencoder (DSA) with a neuron attention stage-by-stage net (NASNet). Malware is finally classified as Gatak, Tracur, Obfuscator.ACY, Simda, Kelihos_ver1, Vundo, Lollipop, ramnit, and Kelihos_ver3. The performance of the NeASA-Net model is validated by comparing it with traditional detection models. Here, the NeASA-Net model achieved superior performance with an accuracy of 92.77%, a true positive rate (TPR) of 95.98%, and a false positive rate (FPR) of 6.54%.
  Keywords: neuron attention stacked autoencoder; NeASA-Net; deep stacked autoencoder; DSA; fractional calculus; neuron attention stage-by-stage net; NASNet; cyber security.
  DOI: 10.1504/IJAMECHS.2025.10071223
   
  
• A comprehensive review of inverse kinematics techniques from analytical foundations to artificial intelligence integration  
  by Navya Manjegowda, Muralidhara, Nirmith R. Jain, Sandeep Kumar Shivaswamy 
  Abstract: Inverse kinematics (IK) is crucial for enabling precise and coordinated motion in robotic systems. This review provides a comprehensive analysis of IK techniques in robotics, examining the traditional analytical, numerical methods and the transformative impact of artificial intelligence (AI)-based approaches. It explains IK fundamentals such as forward kinematics (FK) and joint angles, emphasising the analytical clarity of traditional methods and the adaptability of numerical IK. The evolution of AI-based IK, using machine learning (ML) and neural networks (NNs), is highlighted for its versatility and optimisation in various robotic applications. A comparative analysis outlines the strengths and limitations of traditional and AI-based IK methods, highlighting their efficiency in different scenarios. As robotics advances, the fusion of these IK techniques becomes crucial for navigating complexities and enhancing capabilities. The review underscores the symbiotic integration of classical, numerical, and AI-based IK techniques to meet the demands of modern robotics, fostering improved performance.
  Keywords: robotics; inverse kinematics; traditional methods; machine learning; neural networks; optimisation; motion planning.
  DOI: 10.1504/IJAMECHS.2025.10071298